Description Usage Arguments Value Examples
rcpp_asymHIM_sdetect
computes the asymmetric influence measure
statistic for the multiple detection technique.
1 2 3 4 5 6 7 8 9 10 | rcpp_asymHIM_sdetect(
x,
y,
xquant,
yquant,
inv_rob_sdx,
rob_sdy,
asymvec,
inf_set,
non_inf_set)
|
x |
a matrix of elements. |
y |
a vector of elements. |
xquant |
quantiles of the columns of |
yquant |
quantiles vector of the vector |
inv_rob_sdx |
inverse of the median absolute deviation of the matrix |
rob_sdy |
median absolute deviation of the vector |
asymvec |
vector of asymmetric points or percentiles. |
inf_set |
influential set. |
non_inf_set |
non-influential set. |
A matrix of the influence measure statistic according to the asymmetric points.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | ## Not run:
## Simulate a dataset where the first 10 observations are influentials
require("MASS")
# the vector of asymmetric point
asymvec <- c(0.25,0.5,0.75)
# the parameter of interest
beta_param <- c(3,1.5,0,0,2,rep(0,1000-5))
# the contamination parameter
gama_param <- c(0,0,1,1,0,rep(1,1000-5))
# Covariance matrice for the predictors distribution
sigmain <- diag(rep(1,1000))
for (i in 1:1000)
{
for (j in i:1000)
{
sigmain[i,j] <- 0.5^(abs(j-i))
sigmain[j,i] <- sigmain[i,j]
}
}
# set the seed
set.seed(13)
# the predictor matrix
x <- mvrnorm(100, rep(0, 1000), sigmain)
# the error variable
error_var <- rnorm(100)
# the response variable
y <- x %*% beta_param + error_var
y <- as.numeric(y)
### Generate influential observations
# the contaminated response variable
youtlier <- y
youtlier[1:10] <- x[1:10,] %*% (beta_param + 1.2*gama_param) + error_var[1:10]
youtlier <- as.numeric(youtlier)
# the quantile of the predictors
xquant <- apply(x,2,quantile,asymvec)
# the quantile of contaminated response variable
yquant <- quantile(youtlier,asymvec)
# the inverse of the mad predictors
inv_rob_sdx <- 1/apply(x,2,mad)
# the mad contaminated response variable
rob_sdy <- mad(youtlier)
# influential set
inf_set <- 1:20
# non-influential set
non_inf_set <- 21:100
out <- rcpp_asymHIM_sdetect(x, youtlier, xquant, yquant, inv_rob_sdx,
rob_sdy, asymvec, inf_set, non_inf_set)
## End(Not run)
|
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